Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A supply chain management method of a system that includes a computer port, wherein the computer port comprises a priority assigning device, an inventory checking device and a distributing device, the method comprising: assigning, by the priority assigning device, a first set of priority values based on bucket dates on a time sequence to each of a first set of a customer's forecasted demand numbers on each of the bucket dates; receiving, at the priority assigning device, a second set of the customer's forecasted demand numbers; determining, by the priority assigning device for each of the bucket dates, a difference between each of a corresponding number in the first set of the customer's forecasted demand numbers and a subsequently received second set of the customer's forecasted demand numbers; generating, by the priority assigning device, a second set of priority values for each of the bucket dates: when the first set of the customer's forecasted demand numbers is greater than the second set of the customer's forecasted demand numbers of a first bucket date, assigning a corresponding priority value of the first set of priority values of the first bucket date to the second set of the corresponding customer's forecasted demand numbers, and further allocating the difference to a second bucket date and assigning the priority value of the first bucket date to the difference; and when the first set of the customer's forecasted demand numbers is smaller than the second set of the customer's forecasted demand numbers of the first bucket date, assigning a corresponding value of the first set of priority values of the first bucket date to a number of the first set of the corresponding customer's forecasted demand numbers, and further keeping the difference to the first bucket date while assigning the priority value of the second bucket date to the difference; allocating available resources, with the priority assigning device, according to the second set of the priority values; and controlling the distributing device to package the allocated resources for distribution.
2. The method of claim 1 , wherein the first and second sets of the customer's forecasted demand numbers correspond to amounts of resources required by a customer over a fixed length of time.
This invention relates to a system for managing customer demand forecasting and resource allocation. The method involves generating a first set of forecasted demand numbers for a customer, where these numbers represent the quantities of resources the customer will require over a predefined time period. A second set of forecasted demand numbers is also generated, which similarly corresponds to the customer's resource needs over the same fixed time interval. The method further includes comparing the first and second sets of forecasted demand numbers to identify discrepancies or variations in the customer's projected resource requirements. This comparison helps in optimizing resource allocation, reducing waste, and ensuring that the customer's needs are met efficiently. The system may also involve adjusting the forecasted demand numbers based on historical data, market trends, or other relevant factors to improve accuracy. By analyzing the differences between the two sets of forecasted demand numbers, the system can provide insights into potential fluctuations in demand, allowing for better planning and resource management. The method is particularly useful in industries where precise demand forecasting is critical, such as manufacturing, logistics, and supply chain management.
3. The method of claim 2 , wherein the length of time corresponds to a day, a week, a month, or a multi-month period.
This invention relates to a method for managing data storage and retrieval in a computing system, particularly for organizing and accessing data over specific time intervals. The method addresses the challenge of efficiently categorizing and retrieving data based on temporal relevance, ensuring that information is stored and accessed in a structured manner aligned with predefined time periods. The method involves storing data in a computing system and associating each data entry with a time-based identifier. This identifier corresponds to a specific length of time, such as a day, week, month, or multi-month period. The system then organizes the data into groups based on these time intervals, allowing users to quickly filter and retrieve information relevant to a particular time frame. The method also includes retrieving the stored data by selecting a desired time period, enabling efficient access to data entries associated with that interval. By structuring data retrieval around these predefined time periods, the method improves data management by reducing search times and enhancing organizational efficiency. The system can be applied in various applications, including but not limited to, financial records, project management, and archival systems, where time-based data organization is critical. The method ensures that data is easily accessible and logically grouped, improving overall system usability and performance.
4. The method of claim 1 , further comprising receiving, at a supplier, forecasted demand numbers for a resource for different customers at different times and with varying frequencies.
A system and method for managing resource allocation in supply chain operations addresses inefficiencies in demand forecasting and resource distribution. The technology focuses on optimizing the allocation of resources, such as raw materials, inventory, or production capacity, to meet varying customer demands across different time periods and frequencies. Traditional systems often struggle with static or inflexible forecasting models, leading to overstocking, shortages, or mismatched supply timelines. The invention enhances demand forecasting by incorporating dynamic, time-sensitive data inputs. A supplier receives forecasted demand numbers for a resource, which are specific to different customers, time intervals, and update frequencies. This allows for real-time adjustments to resource allocation based on fluctuating demand patterns. The system processes these inputs to generate optimized distribution schedules, ensuring resources are allocated efficiently to minimize waste and meet customer requirements. The method may also integrate historical demand data, seasonal trends, and external factors like market conditions to refine forecasts. By dynamically adapting to varying demand frequencies and timeframes, the system improves supply chain responsiveness and reduces operational costs. The technology is particularly useful in industries with high variability in customer demand, such as manufacturing, logistics, and retail. The method ensures that resources are allocated in a way that aligns with actual demand, reducing excess inventory and improving overall supply chain efficiency.
5. The method of claim 1 , wherein a smaller priority value corresponds to a higher priority.
A system and method for managing task prioritization in computing environments addresses the challenge of efficiently scheduling and executing tasks based on dynamic priorities. The invention assigns numerical priority values to tasks, where a lower numerical value indicates a higher priority. Tasks are processed in ascending order of their priority values, ensuring that higher-priority tasks are executed before lower-priority ones. The system may include a priority queue or scheduler that dynamically adjusts task priorities based on system conditions, such as resource availability or task dependencies. The method ensures that critical tasks are processed first, improving system responsiveness and efficiency. The invention may be applied in operating systems, real-time systems, or distributed computing environments where task prioritization is essential for optimal performance. The system may also include mechanisms to update priority values in real-time, allowing for adaptive scheduling based on changing system demands. This approach enhances overall system performance by minimizing delays for high-priority tasks while maintaining fairness in task execution.
6. The method of claim 1 , wherein the resources comprise machine component parts, raw materials, or machine capacities.
The invention relates to a method for managing resources in a manufacturing or operational environment, specifically addressing the allocation and utilization of various types of resources to optimize efficiency and productivity. The method involves identifying and categorizing resources into distinct groups, including machine component parts, raw materials, and machine capacities. Machine component parts refer to the individual elements that make up machinery or equipment, ensuring they are available when needed for maintenance or operation. Raw materials encompass the input materials required for production processes, ensuring a steady supply to avoid disruptions. Machine capacities refer to the operational limits or capabilities of machinery, such as processing speed, power, or throughput, which must be managed to prevent bottlenecks or underutilization. By systematically categorizing and managing these resources, the method aims to streamline operations, reduce downtime, and enhance overall system performance. The approach ensures that resources are allocated efficiently, preventing shortages or excesses that could hinder production or increase costs. This method is particularly useful in industries where resource availability and utilization directly impact operational success, such as manufacturing, logistics, or supply chain management.
7. The method of claim 1 , wherein the method is automated.
The invention relates to an automated process for performing a specific task, eliminating the need for manual intervention. The method involves a series of steps executed by a system or device without human input, ensuring efficiency, consistency, and reduced error rates. The automation is achieved through predefined algorithms or rules that guide the system's decision-making and actions. This approach is particularly useful in scenarios where repetitive tasks require high precision or where human intervention is impractical due to scale or complexity. The automated method may include data collection, processing, analysis, and execution of actions based on the processed data. By removing manual steps, the system can operate continuously, handle large volumes of data, and adapt to changes in real-time without delays. The automation ensures that the process is repeatable, scalable, and compliant with predefined standards or regulations. This method is applicable across various domains, including manufacturing, data management, customer service, and software development, where efficiency and reliability are critical. The core advantage lies in the system's ability to perform tasks autonomously, improving productivity and reducing operational costs.
8. The method of claim 1 , further comprising communicating with a data store to thereby access the first set of the customer's forecasted demand numbers.
The invention relates to a system for managing customer demand forecasts in a supply chain or inventory management context. The core problem addressed is the need to efficiently access and utilize forecasted demand data for customers to optimize inventory levels, production planning, or resource allocation. The method involves retrieving a first set of forecasted demand numbers for a customer from a data store. This data store could be a database, cloud storage, or any centralized repository containing historical and predictive demand data. The communication process ensures real-time or near-real-time access to the most up-to-date forecast figures, enabling better decision-making for demand planning. By integrating this step, the system enhances the accuracy and timeliness of demand predictions, reducing inefficiencies such as overstocking or stockouts. The method likely supports downstream processes like automated reordering, production scheduling, or supplier coordination, where precise demand data is critical. The data store interaction is a foundational component, ensuring that the forecasted numbers are current and reliable for subsequent calculations or actions within the broader system.
9. The method of claim 8 , further comprising, after assigning a second set of priority values to the differences, generating a new commit according to the second set of priority values.
The invention relates to version control systems used in software development, specifically addressing the challenge of managing and prioritizing changes between different versions of a codebase. The core problem involves efficiently resolving conflicts or differences between versions while maintaining the integrity of the code. The method begins by identifying differences between two versions of a codebase. These differences are initially assigned priority values based on predefined criteria, such as the importance of the changes or their impact on functionality. After this initial prioritization, a second set of priority values is assigned to the differences, likely refining or adjusting the initial priorities based on additional factors or user input. Following the reassignment of priorities, the method generates a new commit that incorporates the differences according to the updated priority values. This new commit represents a reconciled version of the codebase, where conflicts have been resolved in a way that aligns with the prioritized changes. The process ensures that the most critical or relevant modifications are preserved while minimizing unintended side effects or errors introduced by less important changes. This approach streamlines the merging of code versions, reducing manual intervention and improving the efficiency of collaborative software development.
10. The method of claim 9 , further comprising, after generating a new commit, storing the new commit in the data store.
A system and method for managing versioned data in a distributed environment addresses the challenge of efficiently tracking and storing changes to data across multiple nodes. The invention provides a mechanism for generating and storing new commits that represent modifications to a dataset, ensuring consistency and traceability in a distributed system. When a new commit is created, it is stored in a centralized or distributed data store, allowing for version control and retrieval of historical data states. The commit includes metadata such as a unique identifier, timestamp, and a reference to the previous commit, enabling a linear or branching history of changes. This method ensures that all modifications are recorded and can be accessed for auditing, rollback, or synchronization purposes. The system may also include mechanisms for conflict resolution, ensuring that concurrent changes from different nodes are merged correctly. By storing commits in a data store, the invention facilitates efficient version management, scalability, and fault tolerance in distributed data systems.
11. The method of claim 1 , wherein the second set of priority values is dependent on the first set of priority values assigned to the first set of the customer's forecasted demand numbers.
The invention relates to a system for managing customer demand forecasts by assigning priority values to forecasted demand numbers. The system involves two sets of priority values: a first set assigned to a first set of the customer's forecasted demand numbers, and a second set of priority values that are dependent on the first set. This dependency means the second set of priority values is determined based on the values or assignments of the first set, ensuring that the prioritization of demand forecasts is dynamically adjusted according to the initial assignments. The method aims to improve the accuracy and efficiency of demand forecasting by linking the priority of subsequent demand numbers to the initial prioritization, thereby optimizing resource allocation and planning processes. The dependency ensures that the system maintains consistency in how demand is evaluated and prioritized across different sets of forecasted numbers.
12. A customer orders management method of a system that includes a computer port, wherein the computer port comprises a priority assigning device, an inventory checking device and a distributing device, the method comprising: receiving, at the priority assigning device, a first customer's forecasted quantities of a resource for sequential time periods; receiving, at the priority assigning device, a second customer's forecasted quantities of the resource for the sequential time periods; assigning, by the priority assigning device, priority values based on each of bucket dates on the sequential time periods to each of the first and second customer's forecasted quantities corresponding to each of their respective bucket dates; receiving, at the priority assigning device, the second customer's updated forecasted quantities for the sequential time periods; determining, by the priority assigning device for each of the bucket dates, a difference between a corresponding number in the second customer's forecasted quantity and the second customer's updated forecasted quantity; generating, by the priority assigning device, updated priority values for each of the bucket dates: when the second customer's forecasted quantity is greater than the second customer's updated forecasted quantities of a first bucket date, assigning a priority value of a first bucket date to the second customer's updated forecasted quantities, and further allocating the difference to a second bucket date and assigning the priority value of the first bucket date to the difference; when the second customer's forecasted quantity is smaller than the second customer's updated forecasted quantities of the first bucket date, assigning the first set of priority values of the first bucket date to a number of the second customer's forecasted quantity, and further keeping the difference to the first bucket date while assigning the priority value of the second bucket date to the difference; allocating available resources, with the priority assigning device, based on the updated priority values; and controlling the distributing device to package the allocated resources for distribution.
This invention relates to a system for managing customer orders of a resource, particularly in scenarios where forecasted quantities change over time. The system includes a computer port with a priority assigning device, an inventory checking device, and a distributing device. The method involves receiving forecasted quantities of a resource from multiple customers for sequential time periods, referred to as bucket dates. The priority assigning device assigns priority values to each customer's forecasted quantities based on these bucket dates. If a customer updates their forecasted quantities, the system calculates the difference between the original and updated quantities for each bucket date. For increased quantities, the system assigns the original priority value to the updated quantity and allocates the difference to a subsequent bucket date, also assigning the original priority value to this difference. For decreased quantities, the system retains the original priority value for the reduced quantity and assigns a lower priority value to the remaining difference. The system then allocates available resources based on these updated priority values and controls the distributing device to package and distribute the allocated resources. This approach ensures fair and efficient resource allocation when forecasted demands change, preventing disruptions in supply chain management.
13. The method of claim 12 , wherein forecasted quantities for the sequential time periods are assigned sequentially increasing priority values.
The invention relates to a method for forecasting quantities over sequential time periods, where each forecasted quantity is assigned a priority value that increases sequentially with each subsequent time period. This approach ensures that more recent or future forecasts are given higher priority in decision-making processes, such as inventory management, production planning, or resource allocation. The sequential assignment of priority values helps prioritize newer data or projections, which may be more relevant or accurate due to evolving conditions or updated information. By systematically increasing priority, the method aims to improve the efficiency and effectiveness of planning systems that rely on time-sensitive forecasts. This technique could be particularly useful in dynamic environments where the accuracy of forecasts diminishes over time, ensuring that the most up-to-date predictions are given precedence in operational decisions.
14. The method of claim 12 , wherein priority values for the sequential times periods incorporate inherent priority values from previous time periods.
The invention relates to a method for managing priority values in a system that processes tasks or events across sequential time periods. The core problem addressed is how to dynamically adjust priority values for current time periods based on the inherent priority values from prior periods, ensuring that historical context influences current decision-making. The method involves assigning priority values to tasks or events for each time period in a sequence. These priority values are not static; instead, they incorporate the inherent priority values from previous time periods. This means that the priority of a task or event in the current period is influenced by its priority in earlier periods, allowing for a cumulative or evolving assessment of importance. The approach ensures that tasks with sustained or increasing relevance over time are given appropriate consideration, while those that lose relevance are deprioritized. By integrating historical priority data into current calculations, the method provides a more nuanced and adaptive way to manage priorities, which can be particularly useful in dynamic environments where task importance fluctuates over time. This technique enhances the accuracy and responsiveness of priority-based systems, such as scheduling algorithms, task management platforms, or resource allocation frameworks.
15. The method of claim 12 , further comprising storing the updated priority values and the differences for subsequent cycles of demand evaluation.
A system and method for managing and prioritizing demand in a resource allocation framework. The invention addresses the challenge of dynamically adjusting demand priorities based on real-time conditions, ensuring efficient resource utilization and optimal performance. The method involves evaluating demand requests, determining priority values for each request, and adjusting these values based on system constraints or external factors. The adjusted priorities are then used to reorder or reprioritize the demands, improving decision-making in resource allocation. Additionally, the method includes storing the updated priority values and the differences between original and adjusted values for future reference. This stored data enables historical analysis, trend identification, and iterative refinement of demand evaluation processes across multiple cycles. The system may integrate with existing resource management platforms, providing flexibility in deployment. The invention is particularly useful in industries where demand fluctuates, such as manufacturing, logistics, or cloud computing, where adaptive prioritization enhances operational efficiency.
16. The method of claim 12 , the method is automated.
A system and method for automated data processing involves analyzing input data to identify patterns or anomalies. The method includes receiving input data from one or more sources, such as sensors, databases, or user inputs. The data is then processed using predefined algorithms or machine learning models to detect specific patterns, trends, or deviations. The system may apply filtering techniques to remove noise or irrelevant information before analysis. Once patterns or anomalies are identified, the system generates output data, such as alerts, reports, or recommendations, based on the analysis. The output may be transmitted to a user interface, a storage system, or another processing module for further action. The method is fully automated, requiring no manual intervention during the data processing steps. The system may also include feedback mechanisms to refine the analysis over time, improving accuracy and efficiency. The automated nature of the method ensures consistent and timely processing of large datasets, reducing the need for human oversight.
17. The method of claim 12 , wherein the updated priority values are dependent on the priority values previously assigned to the second customer's forecasted quantities.
The invention relates to a system for dynamically adjusting priority values in a forecasting or resource allocation process, specifically where updated priority values are recalculated based on previously assigned priority values for a second customer's forecasted quantities. The system involves a method where priority values are not static but are iteratively refined using historical or prior priority assignments as a reference point. This ensures that adjustments to priority values account for past decisions, improving consistency and accuracy in forecasting or allocation tasks. The approach may be applied in supply chain management, production planning, or inventory optimization, where customer demand forecasting requires adaptive prioritization. By incorporating previously assigned priority values into the calculation of new priorities, the system enhances decision-making by maintaining continuity with prior assessments while adapting to changing conditions. The method avoids redundant recalculations by leveraging prior priority data, reducing computational overhead and improving efficiency in dynamic environments. This technique is particularly useful in scenarios where multiple customers or demand sources compete for limited resources, and priority adjustments must balance fairness, efficiency, and historical precedence.
18. A supply chain management method of a system that includes a computer port, wherein the computer port comprises a priority assigning device, an inventory checking device and a distributing device, the method comprising: assigning, by the priority assigning device, a first set of priority values based on bucket dates on a time sequence to each of a first set of a customer's forecasted demand numbers corresponding to a respective bucket date; receiving, at the priority assigning device, a second set of customer's forecasted demand numbers; determining, for each of the bucket dates, a difference between a corresponding number in the first set of customer's forecasted demand numbers and a subsequently received second set of customer's forecasted demand numbers; generating, by the priority assigning device, a second set of priority values for each of the bucket dates: when the first set of customer's forecasted demand numbers is greater than the subsequently received second set of customer's forecasted demand numbers of a first bucket date, assigning the first set of priority values of the first bucket date to the subsequently received second set of customer's forecasted demand numbers, and further allocating the difference to a second bucket date and assigning the priority value of the first bucket date to the difference; and when the first set of customer's forecasted demand numbers is smaller than the subsequently received second set of customer's forecasted demand numbers of the first bucket date, assigning a first set of priority values of a first bucket date to a number of the first set of customer's forecasted demand number, and further keeping the difference to the first bucket date while assigning the priority value of the second bucket date to the difference; allocating available resources, with the priority assigning device, according to the second set of the priority values and to the differences; and controlling the distributing device to package the allocated resources for distribution.
Supply chain management system and method for dynamically prioritizing and allocating resources based on updated customer demand forecasts. The system includes a computer port with three integrated devices: a priority assigning device, an inventory checking device, and a distributing device. The method begins by assigning initial priority values to a customer's forecasted demand numbers across time-sequenced bucket dates. When new forecast data is received, the system calculates differences between the original and updated forecasts for each bucket date. If the updated forecast for a given date is lower than originally predicted, the excess demand is reallocated to a subsequent date while maintaining the original priority value. Conversely, if the updated forecast is higher, the shortfall remains in the original date while the priority value shifts to a later date. The system then uses these adjusted priority values and differences to allocate available resources accordingly. Finally, the distributing device packages the allocated resources for shipment. This approach ensures that resource allocation remains responsive to real-time demand changes while maintaining efficient distribution scheduling.
19. The method of claim 18 , wherein the second set of the customer's forecasted demand numbers indicate a forecast of future needs.
A system and method for demand forecasting and inventory management analyzes historical sales data to generate a first set of demand numbers representing past customer demand. The system then processes additional data, such as market trends, seasonal patterns, or external factors, to produce a second set of demand numbers that forecast future customer needs. The second set of demand numbers is used to optimize inventory levels, production planning, or supply chain operations. The system may also compare the first and second sets of demand numbers to identify discrepancies, which can trigger adjustments in procurement, manufacturing, or distribution strategies. The method ensures that inventory is aligned with anticipated demand, reducing stockouts or excess inventory. The system may integrate with enterprise resource planning (ERP) or supply chain management (SCM) software to automate decision-making processes. The forecasting model can be updated dynamically based on real-time data inputs, improving accuracy over time. The method is particularly useful for businesses dealing with variable or seasonal demand, ensuring efficient resource allocation and cost savings.
20. The method of claim 19 , further comprising assigning the first set of priority values to numbers of the second set of forecasted demand numbers that are no more than the first set of forecasted demand numbers.
The invention relates to demand forecasting and prioritization in supply chain or inventory management systems. The problem addressed is the need to efficiently allocate resources or prioritize actions based on forecasted demand data to optimize operations, reduce waste, or improve responsiveness. The method involves generating a set of forecasted demand numbers representing expected future demand for a product or service. A second set of forecasted demand numbers is also generated, which may be derived from the first set or represent a different time horizon, granularity, or scenario. The method further includes assigning a first set of priority values to numbers in the second set of forecasted demand numbers that are no more than the corresponding numbers in the first set. This prioritization helps identify demand levels that are within or below expected thresholds, allowing for targeted decision-making, such as inventory adjustments, production scheduling, or resource allocation. The prioritization step ensures that lower demand forecasts are flagged for attention, enabling proactive measures to avoid overstocking or underutilization of resources. The method may also involve comparing the two sets of forecasted demand numbers to identify discrepancies or trends, which can inform strategic adjustments in supply chain operations. The invention aims to enhance efficiency and accuracy in demand planning by leveraging prioritized forecast data.
Unknown
December 29, 2020
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